Throughput limitaion in spark standalone of simple task without calculation.

I install a spark standalone and run the spark cluster(one master and one worker) in a windows 2008 server with 16cores and 24GB memory.

I have done a simple test: Just create a string RDD and simply return it. I use JMeter to test throughput but the highest is around 35/sec. I think spark is powerful at distribute calculation, but why the throughput is so limit in such simple test scenario only contains simple task dispatch and no calculation?

1. In JMeter I test both 10 threads or 100 threads, there is little difference around 2-3/sec.
2. I test both cache/not cache the RDD, there is little difference around 1-2/sec.
3. During the test, the cpu and memory is in low level.

Below is my test code:
@RestController
public class SimpleTest {
@RequestMapping(value = "/SimpleTest", method = RequestMethod.GET)
@ResponseBody
public String testProcessTransaction()

{ return SparkShardTest.simpleRDDTest(); }

}

final static Map<String, JavaRDD<String>> simpleRDDs = initSimpleRDDs();
public static Map<String, JavaRDD<String>> initSimpleRDDs()

{ Map<String, JavaRDD<String>> result = new ConcurrentHashMap<String, JavaRDD<String>>(); JavaRDD<String> rddData = JavaSC.parallelize(data; rddData.cache().count(); //this cache will improve 1-2/sec result.put("MyRDD", rddData); return result; }

public static String simpleRDDTest()

{ JavaRDD<String> rddData = simpleRDDs.get("MyRDD"); return rddData.first(); }